LONG BEACH, CALIFORNIA--Every time you talk, your body moves in sync, whether it's something as subtle as eyes widening or more extreme movements like flailing arms. Now, researchers have designed an artificial intelligence that knows how you're going to move based purely on the sound of your voice. Researchers collected 144 hours of video of 10 people speaking, including a nun, a chemistry teacher, and five TV show hosts (Conan O'Brien, Ellen DeGeneres, John Oliver, Jon Stewart, and Seth Meyers). They used an existing algorithm to produce skeletal figures representing the positions of the speakers' arms and hands. They then trained their own algorithm with the data, so it would predict gestures based on fresh audio of the speakers.

This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations. Though these methods have been shown quite effective, they lack good explanations, which are critical to recommender systems. In this paper, we take a different path and propose generating recommendations by finding meaningful paths from users to items. Specifically, we formulate the problem as a sequential decision process, where the target user is defined as the initial state, and the walks on the graphs are defined as actions. We shape the rewards according to existing state-of-the-art methods and then train a policy function with policy gradient methods. Experimental results on three real-world datasets show that our proposed method not only provides effective recommendations but also offers good explanations .

All those topics have made headlines this year--in Education Week and elsewhere--and all of them are splashed over the agenda of the country's largest education technology conference, which kicks off in Philadelphia this weekend. The International Society for Technology in Education will draw thousands of teachers, school administrators, and researchers from across the world, not to mention the dozens of ed-tech companies hungry for a piece of the K-12 market. Ben Herold, an ISTE veteran, will be moderating a panel on meeting the ed tech needs of extraordinary students. And I'll be at ISTE for the first time! Follow me on Twitter at @AlysonRKlein.

Summer is coming, and like a lot of parents I'm looking for fun and affordable summer activities to keep my seven and nine-year-old sons occupied. I'm especially interested in activities that get them outside, make them think, or simply get them away from their screens. While I'm sure there will be plenty of hours spent playing video games and watching YouTube Kids, I'm hoping to keep my kids engaged with a variety of fun and affordable summertime activities. Here are 10 of our favorite summertime activities that will get your kids outside and off their devices. Gardening is a healthy and pleasurable hobby whether you live in the country or in a suburban neighborhood.

At the end of January 2019, it suddenly dawned on me that my understanding of artificial intelligence was insufficient. It is increasingly impacting our every day. AI defends our inboxes from spam, it powers weather updates from Alexa, it enables Amazon to recommend a purchase or Netflix to suggest a movie. Every time we open Twitter or Facebook, it's human versus an AI that knows us better than we know ourselves. Yet here I was -- a professed technologist with so little awareness of what AI actually is.

"Machine Learning: Living in the Age of AI," examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup. Still haven't subscribed to WIRED on YouTube? http://wrd.cm/15fP7B7 Here you can find your favorite WIRED shows and new episodes of our latest hit series Tradecraft.

Another day, another deepfake: but this time they can sing. New research from Imperial College in London and Samsung's AI research center in the UK shows how a single photo and audio file can be used to generate a singing or talking video portrait. Like previous deepfake programs we've seen, the researchers uses machine learning to generate their output. And although the fakes are far from 100 percent realistic, the results are amazing considering how little data is needed. Getting a bit wackier, why not have everyone's favorite mad monk, Grigori Yefimovich Rasputin, belting out the Beyoncé classic'Halo'?

Breakthroughs in neural networks--a type of machine learning that vaguely imitates the structure of neurons in the brain--have given rise to a form of the technology called generative AI that can do everything from imitate photorealistic images and abstract art to composing music or writing. As the cultural debate around AI-fueled art begins to heat up, we're looking back on what kind of work has actually come out of the initial experiments in this space. Here are six examples of AI's use in creative processes that offer a sense of the current state of the technology and a hint at its larger potential: Google's DeepDream computer vision software, first released in 2015, turns any image into an abstract hallucinogenic version of itself by finding and enhancing certain patterns within the image. While the system might have little practical use for creative professionals on its face, it represented an early foray into the type of AI-generated art that has come to proliferate the open-source community. One of the most important breakthroughs in AI art also came from then-Google AI researcher Ian Goodfellow in a 2014 paper in which he formalized the structure for something called a generative adversarial network, a key tool in AI-created content.

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. The conventional approach, for both context-aware and standard models, is to retrieve items and apply the constraints as independent operations. The order in which these two steps are executed can induce significant problems. For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i.e., less popular items). As a result, the additional information that the constraint brings about user intent may not be accurately captured. In this paper we propose integrating the information provided by the contextual constraint into the similarity computation, by merging constraint application and retrieval into one operation in the embedding space. This technique allows us to generate high-quality recommendations for the specified constraint. Our approach learns constraints representations jointly with the user and item embeddings. We incorporate our methods into a matrix factorization model, and perform an experimental evaluation on one internal and two real-world datasets. Our results show significant improvements in predictive performance compared to context-aware and standard models.

In this paper we explore techniques for generating new music using a Variational Autoencoder (VAE) neural network that was trained on a corpus of specific style. Instead of randomly sampling the latent states of the network to produce free improvisation, we generate new music by querying the network with musical input in a style different from the training corpus. This allows us to produce new musical output with longer-term structure that blends aspects of the query to the style of the network. In order to control the level of this blending we add a noisy channel between the VAE encoder and decoder using bit-allocation algorithm from communication rate-distortion theory. Our experiments provide new insight into relations between the representational and structural information of latent states and the query signal, suggesting their possible use for composition purposes.